Human variant of scavenger receptor BI (R174C) exhibits impaired cholesterol transport functions
Bibliographic record
Abstract
HDL and its primary receptor, scavenger receptor class B type I (SR-BI), work together to promote the clearance of excess plasma cholesterol, thereby protecting against atherosclerosis. Human variants of SR-BI have been identified in patients with high HDL-cholesterol levels, and at least one variant has been linked to cardiovascular disease. Therefore, while often regarded as beneficial, very high levels of HDL-cholesterol may result from impaired cholesterol clearance through SR-BI and contribute to cardiovascular risk. In this study, we characterized the function of a rare human variant of SR-BI, resulting in the substitution of arginine-174 with cysteine (R174C), which was previously identified in a heterozygous individual with high levels of HDL-cholesterol. We hypothesized that the R174C-SR-BI variant has impaired cholesterol transport functions, which were assessed in COS-7 cells after transient transfection with full-length WT or R174C-SR-BI. Although R174C-SR-BI was expressed at levels comparable to the WT receptor, HDL binding, cholesteryl hexadecyl ether uptake, free cholesterol efflux, and modulation of membrane cholesterol were disrupted in the presence of R174C-SR-BI. We further examined the role of salt bridges as a potential mechanism for R174C-SR-BI dysfunction. If translatable, this human variant could lead to increased plasma HDL-cholesterol levels, impaired cholesterol clearance, and increased cardiovascular disease risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".